


import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


__a 					=   namedtuple(
    '''_TestCommandArgs''',
    [
        '''dataset''',
        '''name''',
        '''cache_dir''',
        '''data_dir''',
        '''all_configs''',
        '''save_infos''',
        '''ignore_verifications''',
        '''force_redownload''',
        '''clear_cache''',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)




def       __lowercase						(    _UpperCamelCase,     _UpperCamelCase  )     ->Any:







      """simple docstring"""

      return (abs(source - target  ) / target) < 0.0_1




@pytest.mark.integration
def       __lowercase						(    _UpperCamelCase  )     ->List[str]:







      """simple docstring"""

      lowercase      : Union[str, Any]        =		_TestCommandArgs(dataset=UpperCamelCase_,     all_configs=UpperCamelCase_,     save_infos=UpperCamelCase_  )
      lowercase      : str        =		TestCommand(*UpperCamelCase_  )
      test_command.run()
      lowercase      : Tuple        =		os.path.join(UpperCamelCase_,     '''README.md'''  )
      assert os.path.exists(UpperCamelCase_  )
      lowercase      : List[Any]        =		DatasetInfosDict.from_directory(UpperCamelCase_  )
      lowercase      : Tuple        =		DatasetInfosDict(
          {
              '''default''': DatasetInfo(
                  features=Features(
                      {
                          '''tokens''': Sequence(Value('''string'''  )  ),
                          '''ner_tags''': Sequence(
                              ClassLabel(names=['''O''', '''B-PER''', '''I-PER''', '''B-ORG''', '''I-ORG''', '''B-LOC''', '''I-LOC''']  )  ),
                          '''langs''': Sequence(Value('''string'''  )  ),
                          '''spans''': Sequence(Value('''string'''  )  ),
                      }  ),     splits=[
                      {
                          '''name''': '''train''',
                          '''num_bytes''': 2351563,
                          '''num_examples''': 10000,
                      },
                      {
                          '''name''': '''validation''',
                          '''num_bytes''': 238418,
                          '''num_examples''': 1000,
                      },
                  ],     download_size=3940680,     dataset_size=2589981,     )
          }  )
      assert dataset_infos.keys() == expected_dataset_infos.keys()
      for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
            lowercase ,      lowercase      : Dict        =		getattr(dataset_infos['''default'''],     UpperCamelCase_  ), getattr(expected_dataset_infos['''default'''],     UpperCamelCase_  )
            if key == "num_bytes":
                  assert is_apercent_close(UpperCamelCase_,     UpperCamelCase_  )
            elif key == "splits":
                  assert list(UpperCamelCase_  ) == list(UpperCamelCase_  )
                  for split in result:
                        assert result[split].name == expected[split].name
                        assert result[split].num_examples == expected[split].num_examples
                        assert is_apercent_close(result[split].num_bytes,     expected[split].num_bytes  )
            else:
                  result == expected






'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import _LazyModule


SCREAMING_SNAKE_CASE__     :						Dict	=  {'''tokenization_wav2vec2_phoneme''': ['''Wav2Vec2PhonemeCTCTokenizer''']}


if TYPE_CHECKING:
			from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
			import sys

			SCREAMING_SNAKE_CASE__     :						Dict	=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




